Strengthening inclusive education through targeted literacy and numeracy interventions for student achievement success

This study explores the application of data mining techniques to inform targeted literacy and numeracy interventions in inclusive education settings, aiming to enhance student achievement success. By leveraging educational data mining (EDM) methods, we analyzed a comprehensive dataset of student performance records, demographic information, and learning behavior patterns. Our analysis employed classification, clustering, and association rule mining techniques to identify key predictors of student success and areas where targeted interventions could be most effective. The findings revealed distinct patterns in struggling students' learning behaviors, such as increased struggle with complex tasks and decreased engagement. Three distinct student subgroups were identified: struggling readers, math-anxious students, and students with overall academic resilience. Significant relationships between specific literacy and numeracy skills were also discovered, informing the development of targeted interventions. These interventions, including phonics-based reading programs and math anxiety reduction strategies, demonstrated significant improvements in student achievement outcomes when evaluated using predictive modeling. The study's findings have implications for teacher professional development, educational policy, and the design of inclusive learning environments. By providing actionable insights, data mining techniques can enhance inclusive education, promoting greater student achievement and reducing educational disparities. This research supports data-driven approaches to education, enabling educators to design more effective literacy and numeracy interventions and ultimately fostering a more inclusive and supportive learning environment.